Biology subjects

Marrazzo, G.

Publications and source records attributed to Marrazzo, G..

8 recordsLinked to original sources

bioRxiv2026

Vision-language encoding models reveal an image-computable food-quality dimension in human occipitotemporal cortex

Perceived calorie content contributes to neural representational structure in human ventral visual cortex, yet it remains unclear whether this reflects an abstract nutritional signal or whether perceived calorie is largely recoverable from the visual-semantic structure of the food image itself. In 25 female participants who passively viewed 96 food images during functional MRI, we decomposed perceived calorie ratings into a component predicted from CLIP (Contrastive Language-Image Pretraining) image embeddings, a vision-language model that captures high-level visual-semantic image structure, and a residual component not captured by this CLIP-based prediction. We then tested their respective contributions to neural prediction using cross-validated banded ridge encoding models. The CLIP-predictable component organized foods along a processedness and naturalness dimension, separating raw single-ingredient foods from prepared and energy-dense foods. Adding this component to a visual-semantic baseline improved neural prediction progressively along the ventral visual hierarchy, with the strongest relative contribution in higher-level ventral temporal cortex. These findings indicate that calorie-related encoding in ventral visual cortex is carried mainly by a shared food-quality axis indexing processedness, naturalness, and perceived healthiness, a substantial part of which is recoverable from image-computable visual-semantic structure, rather than providing evidence for an isolated abstract representation of caloric magnitude. Because these results derive from a reanalysis of 25 female participants viewing a fixed set of 96 images, generalization to broader populations and larger, more varied stimulus sets remains to be established.

neuroscience↗

bioRxiv2026

Distributed representational encoding of food attributes in ventral visual cortex

Despite substantial progress in understanding how visual features of food are processed in the brain, it remains unclear how subjective food properties, such as perceived palatability, perceived caloric content, and perceived health value, are reflected in neural representational structure after accounting for visual similarity. Using functional MRI and representational similarity analysis (RSA), we examined visual, subjective, and categorical food representations in 25 healthy young women viewing 96 food images during an orthogonal color-discrimination task. Univariate analyses revealed reliable activation differences between high- and low-calorie foods in visual and ventral temporal cortex, with additional effects in orbitofrontal regions. Surface-based parcel-wise RSA and hypothesis-driven ROI analyses showed that visual models explained representational structure most strongly in early visual cortex and extended into occipitotemporal regions. Food-related models showed smaller but reliable effects in lateral and ventral occipitotemporal cortex: perceived calorie, perceived health, and objective calorie category were all associated with neural representational geometry in LOTC and VOTC. However, partial RSA and commonality analyses indicated that these effects were largely shared rather than separable. Perceived-calorie structure remained reliable after controlling for visual models, but not after controlling for perceived health. Palatability did not show reliable positive representational correspondence. These findings suggest that food-related information is reflected within occipitotemporal visual representational spaces during incidental viewing, but not as an isolated scalar calorie code. Instead, calorie-related effects appear to reflect a broader food-property axis on which energy density, perceived health, category structure, and learned food knowledge covary.

neuroscience↗

bioRxiv2026

Local Motion Is Sufficient for Human Body Movement Perception in High-Level Visual Cortex

Perceiving the movements of other living organisms is a fundamental ability across species. Although several cortical regions are selective for static body features, it is unclear whether these areas also encode motion cues independently of body shapes. Here, we isolated optical flow patterns from naturalistic body movements using synthesized videos that eliminate explicit body shape information. Behavioral results suggest that participants successfully recognized walking directions from the shape-free flow motion stimuli. Using ultra-high field fMRI, we found that three body-motion-related areas, fusiform gyrus (FG), lateral occipitotemporal cortex (LOTC), and posterior superior temporal sulcus (pSTS), were sensitive to both the amount and the spatiotemporal structure of flow motion. Critically, the flow motion effects were significantly correlated to voxel-wise body motion selectivity in FG and LOTC, but not to body shape selectivity in any of the ROIs. These findings identify flow motion as a distinct and behaviorally sufficient cue for body perception, which could dissociate the neural mechanism underlying motion and shape encoding in FG and LOTC.

neuroscience↗

bioRxiv2025

Neural encoding of biomechanically (im)possible human movements in occipitotemporal cortex

Understanding how the human brain processes body movements is essential for clarifying the mechanisms underlying social cognition and interaction. This study investigates the encoding of biomechanically possible and impossible body movements in occipitotemporal cortex using ultra-high field 7Tesla fMRI. By predicting the response of single voxels to impossible/possible movements using a computational modelling approach, our findings demonstrate that a combination of postural, biomechanical, and categorical features significantly predicts neural responses in the ventral visual cortex, particularly within the extrastriate body area (EBA), underscoring the brains sensitivity to biomechanical plausibility. Lastly, these findings highlight the functional heterogeneity of EBA, with specific regions (middle/superior occipital gyri) focusing on detailed biomechanical features and anterior regions (lateral occipital sulcus and inferior temporal gyrus) integrating more abstract, categorical information.

neuroscience↗

bioRxiv2024

Connectivity and functional diversity of different temporo-occipital nodes for action perception

The temporo-occipital cortex (TOC) plays a key role in body and action perception, but current understanding of its functions is still limited. TOC body regions are heterogeneous and their role in action perception is poorly understood. This study adopted data-driven approaches to region selectivity and investigated the connectivity of TOC nodes and the functional network sensitivity for different whole body action videos. In two human 7T fMRI experiments using independent component analysis, four adjacent body selective nodes were detected within the TOC network with distinct connectivity profiles and functional roles. Action type independent connectivity was observed for the posterior-ventral node to the visual cortex, the posterior-dorsal node to the precuneus and the anterior nodes to the frontal cortex. Action specific connectivity modulations were found in middle frontal gyrus for the aggressive condition with increased connectivity to the anterior node and decreased connectivity to the posterior-dorsal node. But for the defensive condition, node-nonspecific enhancement was found for the TOC-cingulate connectivity. By addressing the issue of multiple nodes in the temporo-occipital network we show a functional dissociation of different body selective centres related to the action type and a potential hierarchy within the TOC body network.

neuroscience↗

bioRxiv2022

Voxelwise encoding models of body stimuli reveal a representational gradient from low-level visual features to postural features in extrastriate body area.

Previous research has focused on the role of the extrastriate body area (EBA) in category-specific body representation, but the specific features that are represented in this area are not well understood. This study used ultra-high field fMRI and banded ridge regression to investigate the coding of body images by comparing the performance of three encoding models in predicting brain activity in ventral visual cortex and specifically the EBA. Our results suggest that EBA represents body stimuli based on a combination of low-level visual features and postural features. Author SummaryHistorically, research on body representation in the brain has focused on category-specific representation, using fMRI to investigate the most posterior body selective region, the extrastriate body area (EBA). However, the role of this area in body perception is still not well understood. This study aims to clarify the role of EBA, in coding information about body images. Using ultra-high field neuroimaging (fMRI) and advanced encoding techniques we tested different computational hypotheses to understand how body images are represented in EBA. Our results suggest that EBA represents bodies using a combination of low-level properties and postural information extracted from the stimulus.

neuroscience↗

bioRxiv2022

A large-scale brain network of species-specific dynamic human body perception

This ultrahigh field 7T fMRI study addressed the question of whether there exists a core network of brain areas at the service of different aspects of body perception. Participants viewed naturalistic videos of monkey and human faces, bodies, and objects along with mosaic-scrambled videos for control of low-level features. ICA-based network analysis was conducted to find body and species modulations at both the voxel and the network levels. Among the body areas, the highest species selectivity was found in the middle frontal gyrus and amygdala. Two large-scale networks were highly selective to bodies, dominated by the lateral occipital cortex and right superior temporal sulcus (STS) respectively. The right STS network showed high species selectivity, and its significant human body-induced node connectivity was focused around the extrastriate body area (EBA), STS, temporoparietal junction (TPJ), premotor cortex, and inferior frontal gyrus (IFG). The human body-specific network discovered here may serve as a brain-wide internal model of the human body serving as an entry point for a variety of processes relying on body descriptions as part of their more specific categorization, action, or expression recognition functions.

neuroscience↗

bioRxiv2020

The dynamics of body category and emotion processing in high-level visual, prefrontal and parietal cortex.

Recent studies provide an increasingly understanding of how visual objects categories like faces or bodies are represented in the brain but also raised the question whether a category based or more network inspired models are more powerful. Two important and so far sidestepped issues in this debate concern, first, how major category attributes like the emotional expression directly influence category representation and second, whether category and attribute representation are sensitive to task demands. This study investigated the impact of a crucial category attribute like emotional expression on category activity and whether this varies with the participants task. Using (fMRI) we measured BOLD responses while participants viewed whole body expressions and performed either an explicit (emotion) or an implicit (shape) recognition task. Our results based on multivariate methods show that the type of task is the strongest determinant of brain activity and can be decoded in EBA, VLPFC and IPL. Brain activity was higher for the explicit task condition in VLPFC and was not emotion specific. This pattern suggests that during explicit recognition of the body expression, body category representation may be strengthened, and emotion and action related activity suppressed. Taken together these results stress the importance of the task and of the role of category attributes for understanding the functional organization of high level visual cortex.

neuroscience↗